Microscopic imaging analysis is a crucial component of biochemistry and medicine, with significant progress in accuracy and speed made due to machine learning methods and improved computation. These technical advances can assist researchers adapt microscopy experimental plans online for real-time information of experimental observations. In a new study now published in Nature Communications, Zachary R. Fox and a team of interdisciplinary scientists in France and the U.S., developed MicroMator—an open and flexible software integrated with Python to drive reactive microscopy experiments. Using the experimental framework, they performed dynamic adaptation of fluorescence microscopy on bacteria to show the potential of MicroMator for real-time tracking and targeting experiments at the single-cell level in two primary case studies.
